The adoption and outcomes of ISO 14001 across Korean business firms

University of Minnesota Ph.D. dissertation. July 2014. Major: Sociology. Advisor: Professor Erin L. Kelly. 1 computer file (PDF); ix, 110 pages.Environmental sociologists and organizational scholars have recently been interested in the origins of voluntary environmental programs and their various outcomes. Similarly, this dissertation examines the adoption of ISO 14001 known as the most famous voluntary environmental program and its consequence in the Korean context. More specifically, I situate the motivation to adopt ISO 14001 and its various outcomes in the context of two theoretical frameworks: resource-based view and institutional theory. I begin by using event-history modeling to examine firms' adoption of ISO 14001 in Korea between 1996 and 2011. I find that both resource-based and institutional factors have influenced the diffusion of ISO 14001. By exploring time-related effects, I also find that while resource-based factors are important in the early periods of the diffusion, institutional factors become important in the later periods of the diffusion. I then explore the effects of ISO 14001 on pollutant emissions among facilities in Korea from 2004 to 2011. Using data from the Toxic Release Inventory (TRI) program of Korea, I find that the adoption of ISO 14001 does not affect the changes of emission performance from 2004 to 2011. This finding indicates that ISO 14001 has been adopted as a symbol to show off organizational commitment to societal requests for environmental responsibility, but not as an instrument to become greener. Moreover, this finding suggests that the institutional context favorable to the diffusion of ISO 14001--in particular, the Korean government's active involvement in the diffusion of ISO 1400--is not likely to lead to the improvement in environmental quality. I conclude the dissertation with a discussion of what these two studies tell us about corporate social responsibility in Korea and, broadly, East Asia

Genetic enhancement of behavioral itch responses in mice lacking phosphoinositide 3-kinase-γ (PI3Kγ)

Phosphoinositide 3-kinases (PI3Ks) are important for synaptic plasticity and various brain functions. The only class IB isoform of PI3K, PI3Kγ, has received the most attention due to its unique roles in synaptic plasticity and cognition. However, the potential role of PI3Kγ in sensory transmission, such as pain and itch has not been examined. In this study, we present the evidence for the first time, that genetic deletion of PI3Kγ enhanced scratching behaviours in histamine-dependent and protease-activated receptor 2 (PAR-2)-dependent itch. In contrast, PI3Kγ-deficient mice did not exhibit enhanced scratching in chloroquine-induced itch, suggesting that PI3Kγ selectively contributes to certain types of behavioal itch response. Furthermore, PI3Kγ-deficient mice exhibited normal acute nociceptive responses to thermal and mechanical noxious stimuli. Behavioral licking responses to intraplantar injections of formalin and mechanical allodynia in a chronic inflammatory pain model (CFA) were also not affected by PI3Kγ gene deletion. Our findings indicate that PI3Kγ selectively contributes to behavioral itching induced by histamine and PAR-2 agonist, but not chloroquine agonist

Multiscale study to investigate nanoparticle agglomeration effect on electrical conductivity of nano-SiC reinforced polypropylene matrix composites

The clustering effect of beta-SiC nanoparticles on the electrical conductivity of polypropylene matrix composites was investigated through a new multiscale modeling framework where density functional theory-based first-principles calculation and electron hopping-based numerical homogenization are integrated. According to parametric studies for particle dispersion states, the electrical conductivity of the nanocomposites clearly depends on the dispersion/agglomeration of the nanoparticles. Due to the work function of beta-SiC, agglomerated particles made a greater contribution to improving electrical conductivity when compared to well-dispersed particles. In addition, the microstructure-conductivity relationship was determined using the clustering density. The proposed framework was validated with the reported experimental literature.N

Deep learning aided evaluation for electromechanical properties of complexly structured polymer nanocomposites

In the present work, two types of deep neural networks (DNNs) were employed to establish the structur-e-property relationship of polymer nanocomposites. The trained DNNs based on multiscale analysis results can not only overcome the limitations of the conventional clustering density-based model and multivariate regression models but also exhibit superior performance in evaluating the electromechanical properties of polypropylene matrix composites, wherein spherical SiC nanoparticles were randomly distributed and dispersed. A simple graph convolution network showed better capability than a complex artificial neural network, despite fewer features considered; this implies that the graph convolution network is more appropriate and user-friendly for evaluating the effect of nanoparticle distribution and agglomeration. In addition, the trained graph convolution network can effectively provide mechanical and electrical properties corresponding to large representative volume element (RVE) without a loss of accuracy. The present study demonstrates that deep learning techniques can be put to practical use for the design of next-generation polymer nanocomposite materials

Social Support and COVID-19 Stress Among Immigrants in South Korea

Individuals have been under more stress since the COVID-19 pandemic began than they were before the pandemic. While social support is a known stress buffer among the general population, its impact on stress among vulnerable populations, such as immigrants and those living in rural areas, has received little attention in the context of South Korea. Accordingly, we examined the relationship between different types of social support and COVID-19 stress among young adult immigrants based on where they live (rural vs. urban). We conducted a survey of 300 young adult immigrants aged 25--34 years and analyzed the results. The dependent variable was COVID-19 stress, and the independent variables were four types of social support: emotional, appraisal, instrumental, and informational. We discovered that young adult immigrants in rural areas perceived higher-level social support in all aspects compared with those in urban areas. Furthermore, social support was not related to COVID-19 stress in urban areas, while appraisal support was positively and informational support was negatively related to COVID-19 stress in rural areas. Our findings suggest that a contextualized understanding of social support is critical to understanding COVID-related stress during the COVID-19 pandemic

Estimating APC Model Parameters for Dynamic Intervals Determined Using Change-Point Detection in Continuous Processes in the Petrochemical Industry

Several papers have proven that advanced process controller (APC) systems can save more energy in the process than proportional-integral-differential (PID) controller systems. Therefore, implementing an APC system is ultimately beneficial for saving energy in the plant. In a typical APC system deployment, the APC model parameters are calculated from dynamic data intervals obtained through the plant test. However, depending on the proficiency of the APC engineer, the results of the plant test and the APC model parameters are implemented differently. To minimize the influence of the APC engineer and calculate universal APC model parameters, a technique is needed to obtain dynamic data without a plant test. In this study, we utilize time-series data from a real petrochemical plant to determine dynamic intervals and estimate APC model parameters, which have not been investigated in previous studies. This involves extracting the data of the dynamic intervals with the smallest mean absolute error (MAE) by utilizing statistical techniques such as pruned exact linear time, linear kernel, and radial basis function kernel of change-point detection (CPD). After that, we fix the hyper parameters at the minimum MAE value and estimate the APC model parameters by training with the data from the dynamic intervals. The estimated APC model parameters are applied to the APC program to compare the APC model fitting rate and verify the accuracy of the APC model parameters in the dynamic intervals obtained through CPD. The final validation of the model fitting rates demonstrates that the identification of the dynamic intervals and the estimation of the APC model parameters through CPD show high accuracy. We show that it is possible to estimate APC model parameters from dynamic intervals determined by CPD without a plant test

Individually addressable, high-density vertical nanotube Schottky diode crossbar array

We report on the fabrication of individually addressable, high-density vertical zinc oxide (ZnO) nanotube Schottky diode arrays. The individually addressable nanotube Schottky diode arrays were fabricated by arranging the top and bottom electrodes in a crossbar configuration on a free-standing layer consisting of position-controlled ZnO nanotubes on graphene films. The electrical characteristics of each Schottky diode in the arrays were investigated by measuring current-voltage characteristics. We also investigated the variation in device characteristics within an array by spatially mapping the barrier height of individual devices. Additionally, we further confirmed the excellent flexibility and electrical robustness of the free-standing and thin Schottky diode arrays under extreme bending conditions and over multiple cycles. Moreover, the photoresponses of the nanotube Schottky diode arrays were investigated by measuring their spectral responses and current-voltage characteristics under light illuminations, yielding a maximum photocurrent to dark current ratio of 1400 and responsivity of 10(6) A/W. We believe that this work provides a general and rational route for developing many other two-terminal one-dimensional nanostructure device arrays for ultra-high density electronic and optoelectronic devices

Plasticity of Metabotropic Glutamate Receptor-Dependent Long-Term Depression in the Anterior Cingulate Cortex after Amputation

Long-term depression (LTD) is a key form of synaptic plasticity important in learning and information storage in the brain. It has been studied in various cortical regions, including the anterior cingulate cortex (ACC). ACC is a crucial cortical region involved in such emotion-related physiological and pathological conditions as fear memory and chronic pain. In the present study, we used a multielectrode array system to map cingulate LTD in a spatiotemporal manner within the ACC. We found that low-frequency stimulation (1 Hz, 15 min) applied onto deep layer V induced LTD in layers II/III and layers V/VI. Cingulate LTD requires activation of metabotropic glutamate receptors (mGluRs), while L-type voltage-gated calcium channels and NMDA receptors also contribute to its induction. Peripheral amputation of the distal tail impaired ACC LTD, an effect that persisted for at least 2 weeks. The loss of LTD was rescued by priming ACC slices with activation of mGluR1 receptors by coapplying (RS)-3,5-dihydroxyphenylglycine and MPEP, a form of metaplasticity that involved the activation of protein kinase C. Our results provide in vitro evidence of the spatiotemporal properties of ACC LTD in adult mice. We demonstrate that tail amputation causes LTD impairment within the ACC circuit and that this can be rescued by activation of mGluR1.</p